1,484 research outputs found
Branch-and-Prune Search Strategies for Numerical Constraint Solving
When solving numerical constraints such as nonlinear equations and
inequalities, solvers often exploit pruning techniques, which remove redundant
value combinations from the domains of variables, at pruning steps. To find the
complete solution set, most of these solvers alternate the pruning steps with
branching steps, which split each problem into subproblems. This forms the
so-called branch-and-prune framework, well known among the approaches for
solving numerical constraints. The basic branch-and-prune search strategy that
uses domain bisections in place of the branching steps is called the bisection
search. In general, the bisection search works well in case (i) the solutions
are isolated, but it can be improved further in case (ii) there are continuums
of solutions (this often occurs when inequalities are involved). In this paper,
we propose a new branch-and-prune search strategy along with several variants,
which not only allow yielding better branching decisions in the latter case,
but also work as well as the bisection search does in the former case. These
new search algorithms enable us to employ various pruning techniques in the
construction of inner and outer approximations of the solution set. Our
experiments show that these algorithms speed up the solving process often by
one order of magnitude or more when solving problems with continuums of
solutions, while keeping the same performance as the bisection search when the
solutions are isolated.Comment: 43 pages, 11 figure
pde2path - A Matlab package for continuation and bifurcation in 2D elliptic systems
pde2path is a free and easy to use Matlab continuation/bifurcation package
for elliptic systems of PDEs with arbitrary many components, on general two
dimensional domains, and with rather general boundary conditions. The package
is based on the FEM of the Matlab pdetoolbox, and is explained by a number of
examples, including Bratu's problem, the Schnakenberg model, Rayleigh-Benard
convection, and von Karman plate equations. These serve as templates to study
new problems, for which the user has to provide, via Matlab function files, a
description of the geometry, the boundary conditions, the coefficients of the
PDE, and a rough initial guess of a solution. The basic algorithm is a one
parameter arclength continuation with optional bifurcation detection and
branch-switching. Stability calculations, error control and mesh-handling, and
some elementary time-integration for the associated parabolic problem are also
supported. The continuation, branch-switching, plotting etc are performed via
Matlab command-line function calls guided by the AUTO style. The software can
be downloaded from www.staff.uni-oldenburg.de/hannes.uecker/pde2path, where
also an online documentation of the software is provided such that in this
paper we focus more on the mathematics and the example systems
pde2path - version 2.0: faster FEM, multi-parameter continuation, nonlinear boundary conditions, and periodic domains - a short manual
pdepath 2.0 is an upgrade of the continuation/bifurcation package pde2path
for elliptic systems of PDEs over bounded 2D domains, based on the Matlab
pdetoolbox. The new features include a more efficient use of FEM, easier
switching between different single parameter continuations, genuine
multi-parameter continuation (e.g., fold continuation), more efficient
implementation of nonlinear boundary conditions, cylinder and torus geometries
(i.e., periodic boundary conditions), and a general interface for adding
auxiliary equations like mass conservation or phase equations for continuation
of traveling waves. The package (library, demos, manuals) can be downloaded at
www.staff.uni-oldenburg.de/hannes.uecker/pde2pat
Stochastic Sensor Scheduling via Distributed Convex Optimization
In this paper, we propose a stochastic scheduling strategy for estimating the
states of N discrete-time linear time invariant (DTLTI) dynamic systems, where
only one system can be observed by the sensor at each time instant due to
practical resource constraints. The idea of our stochastic strategy is that a
system is randomly selected for observation at each time instant according to a
pre-assigned probability distribution. We aim to find the optimal pre-assigned
probability in order to minimize the maximal estimate error covariance among
dynamic systems. We first show that under mild conditions, the stochastic
scheduling problem gives an upper bound on the performance of the optimal
sensor selection problem, notoriously difficult to solve. We next relax the
stochastic scheduling problem into a tractable suboptimal quasi-convex form. We
then show that the new problem can be decomposed into coupled small convex
optimization problems, and it can be solved in a distributed fashion. Finally,
for scheduling implementation, we propose centralized and distributed
deterministic scheduling strategies based on the optimal stochastic solution
and provide simulation examples.Comment: Proof errors and typos are fixed. One section is removed from last
versio
Computing one-dimensional stable manifolds of planar maps without the inverse
We present an algorithm to compute the one-dimensional stable manifold of a saddle point for a planar map. In contrast to current standard techniques, here it is not necessary to know the inverse or approximate it, for example, by using Newton's method. Rather than using the inverse, the manifold is grown starting from the linear eigenspace near the saddle point by adding a point that maps back onto an earlier segment of the stable manifold. The performance of the algorithm is compared to other methods using an example in which the inverse map is known explicitly. The strength of our method is illustrated with examples of noninvertible maps, where the stable set may consist of many different pieces, and with a piecewise-smooth model of an interrupted cutting process. The algorithm has been implemented for use in the DsTool environment and is available for download with this paper
Projected Power Iteration for Network Alignment
The network alignment problem asks for the best correspondence between two
given graphs, so that the largest possible number of edges are matched. This
problem appears in many scientific problems (like the study of protein-protein
interactions) and it is very closely related to the quadratic assignment
problem which has graph isomorphism, traveling salesman and minimum bisection
problems as particular cases. The graph matching problem is NP-hard in general.
However, under some restrictive models for the graphs, algorithms can
approximate the alignment efficiently. In that spirit the recent work by Feizi
and collaborators introduce EigenAlign, a fast spectral method with convergence
guarantees for Erd\H{o}s-Reny\'i graphs. In this work we propose the algorithm
Projected Power Alignment, which is a projected power iteration version of
EigenAlign. We numerically show it improves the recovery rates of EigenAlign
and we describe the theory that may be used to provide performance guarantees
for Projected Power Alignment.Comment: 8 page
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